An Intrusion Detection Framework Based on Hybrid Multi-Level Data Mining

作者：Yao, Haipeng^*; Wang, Qiyi; Wang, Luyao; Zhang, Peiying; Li, Maozhen; Liu, Yunjie

来源：International Journal of Parallel Programming, 2019, 47(4): 740-758.

DOI：10.1007/s10766-017-0537-7

• Intrusion detection system
• Multi-level
• Machine learning
• Data engineering
• KDDCUP99

With the dramatic opening-up of network, network security becomes a severe social problem with the rapid development of network technology. Intrusion Detection System (IDS) is an innovative and proactive network security technology, which becomes a hot topic in both industry and academia in recent years. There are four main characteristics of intrusion data that affect the performance of IDS including multicomponent, data imbalance, time-varying and unknown attacks. We propose a novel IDS framework called HMLD to address these issues, which is an exquisite designed framework based on Hybrid Multi-Level Data Mining. In this paper, we use KDDCUP99 dataset to evaluate the performance of HMLD. The experimental results show that HMLD can reach 96.70% accuracy which is nearly 1% higher than the recent proposed optimal algorithm SVM+ELM+Modified K-Means. In details, HMLD greatly increased the detection accuracy of DoS attacks and R2L attacks.

• 出版日期2019-8
• 单位北京工业大学; 北京邮电大学